From 4df20cd08a63d5686e563e4bb0a37bfc36d2abc5 Mon Sep 17 00:00:00 2001 From: sampathkumar8421-netizen Date: Fri, 17 Apr 2026 02:25:57 +0530 Subject: [PATCH] Update and rename README.md to Sphinx --- README.md | 86 ------------------------------------------------------- Sphinx | 50 ++++++++++++++++++++++++++++++++ 2 files changed, 50 insertions(+), 86 deletions(-) delete mode 100644 README.md create mode 100644 Sphinx diff --git a/README.md b/README.md deleted file mode 100644 index c5c886b3e..000000000 --- a/README.md +++ /dev/null @@ -1,86 +0,0 @@ -# HackToFuture 4.0 — Template - -Welcome to your official HackToFuture 4 repository. - -This repository template will be used for development, tracking progress, and final submission of your project. Ensure that all work is committed here within the allowed hackathon duration. - ---- - -### Instructions for the teams: - -- Fork the Repository and name the forked repo in this convention: hacktofuture4-team_id (for eg: hacktofuture4-A01) - ---- - -## Rules - -- Work must be done ONLY in the forked repository -- Only Four Contributors are allowed. -- After 36 hours, Please make PR to the Main Repository. A Form will be sent to fill the required information. -- Do not copy code from other teams -- All commits must be from individual GitHub accounts -- Please provide meaningful commits for tracking. -- Do not share your repository with other teams -- Final submission must be pushed before the deadline -- Any violation may lead to disqualification - ---- - -# The Final README Template - -## Problem Statement / Idea - -Clearly describe the problem you are solving. - -- What is the problem? -- Why is it important? -- Who are the target users? - ---- - -## Proposed Solution - -Explain your approach: - -- What are you building? -- How does it solve the problem? -- What makes your solution unique? - ---- - -## Features - -List the core features of your project: - -- Feature 1 -- Feature 2 -- Feature 3 - ---- - -## Tech Stack - -Mention all technologies used: - -- Frontend: -- Backend: -- Database: -- APIs / Services: -- Tools / Libraries: - ---- - -## Project Setup Instructions - -Provide clear steps to run your project: - -```bash -# Clone the repository -git clone - -# Install dependencies -... - -# Run the project -... -``` diff --git a/Sphinx b/Sphinx new file mode 100644 index 000000000..c79e850b1 --- /dev/null +++ b/Sphinx @@ -0,0 +1,50 @@ +# Team Sphinx | Argo-Agentic Intelligence 🌊 + +## Problem Statement / Idea +* **What is the problem?** +Oceanographic telemetry from the Argo float network is often siloed in complex, technical formats, making it difficult for researchers to query data using natural language or perform high-speed spatial-semantic analysis. + +* **Why is it important?** +Fast access to metrics like ocean heat content and oxygen anomalies is critical for climate adaptation. Current workflows lack the ability to bridge the gap between native language queries (Hindi/Bengali) and technical SQL databases. + +* **Who are the target users?** +Marine data scientists, environmental policy-makers, and researchers requiring offline-first, high-accuracy scientific assistance for deep-ocean telemetry. + +## Proposed Solution +* **What are you building?** +We have built the **Agentic Ocean Intelligence Hyperpipeline**—a localized ML framework and Supabase-ready ingestion system that transforms raw telemetry into a RAG-augmented knowledge base. + +* **How does it solve the problem?** +By fusing **AutoGluon** deterministic models for query routing and **LangChain** for local LLM orchestration, we automate the path from a researcher's natural language question to a precise SQL or vector-based answer. + +* **What makes your solution unique?** +The system is **Sovereign and Cross-Lingual**. It uses **IndicNLP** to translate native queries into English SQL logic and operates entirely on local infrastructure (LM Studio/Ollama) to ensure total data privacy. + +## Features +* **Feature 1: Hybrid Routing Engine** – Uses AutoGluon `TabularPredictor` to categorize queries before passing them to LLMs, ensuring high-speed, zero-hallucination routing. +* **Feature 2: Supabase pgvector/PostGIS Integration** – A high-performance importer that enables both 768-dim semantic search and complex spatial queries (e.g., profiles within 200km). +* **Feature 3: Multi-Turn Scientific Memory** – Implements `ConversationBufferWindowMemory` and DiskCache to maintain context throughout complex research sessions. + +## Tech Stack +* **Frontend:** Streamlit +* **Backend:** Python 3.10, LangChain, LM Studio, Ollama +* **Database:** Supabase (PostgreSQL + pgvector + PostGIS) +* **APIs / Services:** Local LLM API (localhost:1234), Local Ollama (localhost:11434) +* **Tools / Libraries:** AutoGluon, IndicNLP, Pydantic, Pandas, NetCDF4 + +## Project Setup Instructions + +```bash +# 1. Install Dependencies +pip install autogluon streamlit pandas requests pydantic langchain langchain-openai diskcache psycopg2-binary + +# 2. Setup Database Schema +# Ensure 'new.env' is configured with your DIRECT_URL +python argo_import_supabase.py --env-file new.env --data-dir "C:/path/to/data" --setup-only + +# 3. Import Data & Generate Embeddings +# Requires Ollama running 'nomic-embed-text' +python argo_import_supabase.py --env-file new.env --data-dir "C:/path/to/data" --embed + +# 4. Launch Application +python -m streamlit run hyperpipeline/chat.py